An AI reliability platform needs enough evidence to answer the questions your team asks about an AI system—such as why it failed, how an agent used a tool, or whether a release changed output quality. That may include prompts and responses, tool activity, traces, metrics, errors, token usage, and evaluation results. Give each person and automated workflow only the data and permissions it needs; conversation content should not be exposed by default when operational signals will do.
What data should an AI reliability platform collect?
Start with the reliability question, then collect the least sensitive data that can answer it. A platform used only to monitor service health may not need to retain conversation content. Investigating answer quality or safety may require approved access to prompts and responses.
| Data category | What it helps answer | Considerations |
|---|---|---|
| Prompts and responses | Whether the system produced a useful, safe, or unexpected answer | May contain personal, confidential, or proprietary information. Govern capture, access, sharing, and retention deliberately. Google Cloud; Microsoft |
| Tool and API activity | What an agent attempted, what tools returned, and where execution failed or slowed | Record relevant calls, outcomes, latency, errors, and data exchanged with tools. Google Cloud |
| Operational telemetry | Service health, performance, debugging, and cost patterns | Useful signals include traces, logs, error rates, latency, and token usage. Google Cloud |
| Evaluation metrics and results | Whether behavior changed across releases or evaluation runs | Associate results with the model and dataset versions used. Google Cloud Architecture Center |
| Audit and lineage records | Who accessed data, what configuration changed, and which inputs and versions contributed to an output | Keep access and configuration evidence, and connect relevant data, model, and code versions. Google Cloud Architecture Center |
These are design options, not a requirement to retain every field. Match collection to the platform’s role: health monitoring, quality investigation, safety evaluation, incident correlation, or autonomous action.
Which permissions and roles should be separate?
Separate access according to what a person or process needs to do. A read-only observer, a content investigator, and an automated agent have different risk profiles.
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- Health and analytics readers: Let operations staff view aggregate analytics and traces without automatically granting conversation access. Grafana documents a data-reader role that can access analytics, traces, model cards, agents, evaluation results, and experiments without access to conversations. Grafana Labs
- Conversation readers: Grant content access only to people who need it for a defined quality or incident task, with suitable organizational approval and scope. Grafana documents conversation-read permission separately. Grafana Labs
- Feedback writers: Keep the ability to submit feedback distinct from the ability to read conversations where the product supports it. Grafana documents a separate feedback-writer role. Grafana Labs
- Evaluators and administrators: Separate evaluator, guard, settings, and other configuration or write permissions from read-only investigation. Grafana Labs
- Autonomous service identities: Use a dedicated identity with an explicit resource scope and only the write access needed for its task. In Microsoft’s Azure Copilot Observability Agent, interactive workflows use the signed-in user’s Azure RBAC permissions, while autonomous operations use the resource’s managed identity and configured scope. The documented example calls for Monitoring Contributor on the Azure Monitor Workspace where issues are created. Microsoft
- Platform setup and API enablement: Keep infrastructure administration separate from ordinary observation. Google Cloud’s Application Monitoring documentation describes additional permissions for enabling APIs and distinct viewer permissions for reading observability data. Google Cloud
Google Cloud’s AI/ML reliability guidance recommends minimum necessary permissions and consistent IAM policies across data storage, model resources, and compute. For example, a training service account may need to read training data and write model artifacts without receiving write access to production serving endpoints. Google Cloud Architecture Center
How should you handle privacy, retention, and data sharing?
Before enabling capture or sending information to an external model provider, identify the data involved, the purpose, the controlling identity, the service scope, and the applicable contractual and organizational controls. Decide whether conversation content is actually needed; metrics and traces may be enough for some investigations.
Check whether the specific service supports the controls you require. Microsoft says its Azure Copilot Observability Agent does not use customer data to train models and limits model-visible data through scope and permissions. Its FAQ also says users cannot selectively exclude individual telemetry fields within an in-scope resource. Microsoft
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OpenAI’s API data-sharing controls are optional and managed by organization or project. Its guidance covers sharing feedback, evaluation and fine-tuning data, and API inputs and outputs; it requires appropriate permissions and cautions against sharing sensitive, confidential, or proprietary information through that mechanism. These statements apply to the named services, not every provider. Confirm current terms and configuration for the exact product, plan, geography, and deployment, including retention and deletion behavior. OpenAI
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An investigator should be able to determine which identity accessed a dataset, trace, prompt, or endpoint; what configuration changed; what scope applied; and which model, data, and code versions were involved. Google Cloud recommends Cloud Audit Logs for API calls, data-access events, and configuration changes, with monitoring and export options for security analysis. Its architecture guidance also recommends catalogs and lineage that connect datasets, model versions, code, and evaluation metrics. Google Cloud Architecture Center
Agent traces can show tool use and the sequence of recorded activity, but a generated explanation should not be treated as proof that the system’s internal reasoning was faithfully captured. Use direct event records, access logs, and version records for accountability. The cited guidance does not establish a universal retention period or legal retention rule.
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How do you compare AI reliability platforms?
Use these questions to assess candidates against your reliability needs and risk controls:
- Signal coverage: Can it capture the needed prompts and responses, tool calls and exchanged data, traces, metrics, errors, token usage, and evaluation evidence?
- Content separation: Can staff inspect analytics and traces without seeing conversations? Can access be limited by project, resource, or view?
- Identity and autonomy: Does interactive access use the signed-in identity? Do autonomous jobs use a separate identity with a narrowly configured scope?
- Data handling: What are the exact controls for model-training use, provider sharing, residency, retention, deletion, redaction, and field-level filtering? Verify them for the product and region rather than assuming vendors work alike.
- Audit and lineage: Are access and configuration events available and exportable? Can outputs and evaluations be connected to relevant model, data, and code versions?
- Write permissions: Are read-only observers, feedback authors, evaluators, guard administrators, and platform administrators granted distinct permissions?
Prefer a platform and configuration that answer the questions your team actually needs to investigate while minimizing sensitive collection and access.
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